ALI Performance Tests on Blake¶
Introduction¶
Currently testing the Greenland Ice Sheet (GIS) and Antartica Ice Sheet (AIS) in Albany Land Ice (ALI) using Intel Skylake CPUs on blake.
Architectures:¶
| Name | Blake (SKX) |
|---|---|
| CPU | Dual-socket Intel Xeon Platinum 8160 (Skylake) |
| Cores/Node | 48 |
| Threads/Core | 2 |
| Memory/Node | 187 GB |
| Interconnect | Intel OmniPath Gen-1 (100 GB/s) |
| Compiler | Intel 18.1.163 |
| MPI | openmpi 2.1.2 |
Cases:¶
| Case Name | Number of Processes (np) | Description |
|---|---|---|
| ant-2-20km_ml_ls | 384 | Unstructured 2-20km AIS, ML w/ line smoothing |
| ant-2-20km_mu_ls | 384 | Unstructured 2-20km AIS, MueLu w/ line smoothing |
| ant-2-20km_mu_dls | 384 | Unstructured 2-20km AIS, MueLu w/ decoupled line smoothing |
| greenland-1-7km_fea_1ws | 384 | Unstructured 1-7km GIS, finite element assembly only, single workset |
| greenland-1-7km_ml_ls_1ws | 384 | Unstructured 1-7km GIS, ML w/ line smoothing, single workset |
| greenland-1-7km_mu_ls_1ws | 384 | Unstructured 1-7km GIS, MueLu w/ line smoothing, single workset |
| greenland-1-7km_mu_dls_1ws | 384 | Unstructured 1-7km GIS, MueLu w/ decoupled line smoothing, single workset |
| greenland-1-7km_fea_mem | 384 | Unstructured 1-7km GIS, finite element assembly only, memoization |
| greenland-1-7km_ml_ls_mem | 384 | Unstructured 1-7km GIS, ML w/ line smoothing, memoization |
| greenland-1-7km_mu_ls_mem | 384 | Unstructured 1-7km GIS, MueLu w/ line smoothing, memoization |
| greenland-1-7km_mu_dls_mem | 384 | Unstructured 1-7km GIS, MueLu w/ decoupled line smoothing, memoization |
In [1]:
import datetime as dt
import glob
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import os
# Import scripts
from json2status import json2status
from json2timeline import json2timeline
# Extract file names
files = glob.glob('ctest-*')
Performance Tests Status¶
In [2]:
%matplotlib inline
#plt.rc('text', usetex=True)
plt.rc('font', family='serif', size=24)
plt.rcParams['figure.figsize'] = (14, 6)
# Cases to plot
cases = ('ant-2-20km_ml_ls',
'ant-2-20km_mu_ls',
'ant-2-20km_mu_dls',
'green-1-7km_fea_1ws',
'green-1-7km_ml_ls_1ws',
'green-1-7km_mu_ls_1ws',
'green-1-7km_mu_dls_1ws',
'green-1-7km_fea_mem',
'green-1-7km_ml_ls_mem',
'green-1-7km_mu_ls_mem',
'green-1-7km_mu_dls_mem')
np = 384
# Plot:
plt.figure()
imarker = 0
markers = ['+','o','*','x','v','d','^','s','>','<','+']
legNames = []
for case in cases:
# Extract info and plot
dates, status = json2status(files, case, np)
fdates = [dt.datetime.strptime(str(d),'%Y%m%d').date() for d in dates]
plt.plot(fdates, status, markersize=8.0, marker=markers[imarker], linestyle='None')
imarker = imarker + 1
legNames.append(case+'-np'+str(np))
plt.xlabel('Date')
plt.ylabel('Pass/Fail')
#plt.ylim((6e-3, 1e-1))
plt.legend(legNames,fontsize=14,loc='center left')
plt.tight_layout()
plt.title('Performance Tests Status')
ax = plt.gca()
ax.set_axisbelow(True)
ax.grid(linestyle='--', alpha=0.5)
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
#ax.tick_params(axis='x',rotation = 45)
plt.show()
Performance Timelines¶
In [3]:
%matplotlib inline
#plt.rc('text', usetex=True)
plt.rc('font', family='serif', size=24)
plt.rcParams['figure.figsize'] = (12, 6)
# Cases to plot:
cases = ('ant-2-20km_ml_ls',
'ant-2-20km_mu_ls',
'ant-2-20km_mu_dls',
'green-1-7km_fea_1ws',
'green-1-7km_ml_ls_1ws',
'green-1-7km_mu_ls_1ws',
'green-1-7km_mu_dls_1ws',
'green-1-7km_fea_mem',
'green-1-7km_ml_ls_mem',
'green-1-7km_mu_ls_mem',
'green-1-7km_mu_dls_mem')
np = 384
names = ('Total Time',
'Setup Time',
'Total Fill Time',
'Residual Fill',
'Residual Fill Evaluate',
'Residual Fill Export',
'Jacobian Fill',
'Jacobian Fill Evaluate',
'Jacobian Fill Export',
'NOX Total Preconditioner Construction',
'NOX Total Linear Solve')
timers = ('Albany Total Time:',
'Albany: Setup Time:',
'Albany: Total Fill Time:',
'Albany Fill: Residual:',
'Albany Residual Fill: Evaluate:',
'Albany Residual Fill: Export:',
'Albany Fill: Jacobian:',
'Albany Jacobian Fill: Evaluate:',
'Albany Jacobian Fill: Export:',
'NOX Total Preconditioner Construction:',
'NOX Total Linear Solve:')
# Plot:
for case in cases:
for name, timer in zip(names, timers):
dates, wtimes = json2timeline(files, case, np, timer, False)
fdates = [dt.datetime.strptime(str(d),'%Y%m%d').date() for d in dates]
# Plot
plt.figure()
plt.plot(fdates, wtimes, linewidth=3.0)
plt.xlabel('Date')
plt.ylabel('Wall-clock time (s)')
#plt.ylim((6e-3, 1e-1))
plt.tight_layout()
plt.title(case + ' timeline - ' + name)
ax = plt.gca()
ax.set_axisbelow(True)
ax.grid(linestyle='--', alpha=0.5)
ax.xaxis.set_major_locator(plt.MaxNLocator(5))
#ax.tick_params(axis='x',rotation = 45)
plt.show()
# Print
print("Dates: ", dates)
print("Wall-clock Time: ", wtimes)
